globalchange  > 影响、适应和脆弱性
DOI: 10.1016/j.atmosres.2018.05.012
Scopus记录号: 2-s2.0-85047606629
论文题名:
Input selection and data-driven model performance optimization to predict the Standardized Precipitation and Evaporation Index in a drought-prone region
作者: Mouatadid S.; Raj N.; Deo R.C.; Adamowski J.F.
刊名: Atmospheric Research
ISSN: 1698095
出版年: 2018
卷: 212
起始页码: 130
结束页码: 149
语种: 英语
英文关键词: Australia ; Drought prediction ; Hydrological drought ; Machine learning ; Standardized Precipitation and Evapotranspiration Index (SPEI) ; Water management
Scopus关键词: Evapotranspiration ; Forecasting ; Learning systems ; Linear regression ; Mean square error ; Neural networks ; Water management ; Australia ; Hydrological droughts ; Multiple linear regressions ; Standardized Precipitation and Evapotranspiration Index (SPEI) ; Standardized precipitation index ; Support vector regression (SVR) ; Water resources assessment ; World meteorological organizations ; Drought
英文摘要: Accurate predictions of drought events to plan and manage the adverse effects of drought on agriculture and the environment requires tools that precisely predict standardized drought metrics. Improving on the World Meteorological Organization approved Standardized Precipitation Index (SPI), the multi-scalar Standardized Precipitation and Evapotranspiration Index (SPEI), a variant of the SPI, is a relatively recent drought index, which takes into account the impacts of temperature change on overall dryness, along with precipitation and evapotranspiration effects. In this paper, an extreme learning machine (ELM) model was applied to predict SPEI in a drought-prone region in eastern Australia, and the quality of the model's performance was compared to that of a multiple linear regression (MLR), an artificial neural network (ANN), and a least support vector regression (LSSVR) model. The SPEI data were derived from climatic variables recorded at six weather stations between January 1915 and December 2012. Model performance was evaluated by means of the normalized root mean square error (NRMSE), normalized mean absolute error (NMAE), coefficients of determination (r2), and the Nash-Sutcliffe efficiency coefficient (NASH) in the testing period. Results showed that the ELM and ANN models outperformed the MLR and LSSVR models, and all four models revealed a greater predictive accuracy for the 12-month compared to the 3-month SPEI predictions. For the 12-month SPEI predictions, optimal models had r2 that ranged from 0.668 for the LSSVR model (Station 6) to 0.894 for the ANN model (Station 4). The good agreement between observed and predicted SPEI at different locations within the study region indicated the potential of the developed models to contribute to a more thorough understanding of potential future drought-risks in eastern Australia, and their applicability to drought assessments over multiple timescales. The models and findings have useful implications for water resources assessment in drought-prone regions. © 2018
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/108832
Appears in Collections:影响、适应和脆弱性
气候变化事实与影响

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作者单位: Department of Computer Science, University of Toronto, Toronto, ON M5S, Canada; School of Agricultural Computational and Environmental Sciences, International Centre for Applied Climate Sciences, Institute of Agriculture and Environment, University of Southern Queensland, Springfield, QLD 4300, Australia; Department of Bioresource Engineering, McGill University, Sainte-Anne-de-Bellevue, QC H9X 3V9, Canada

Recommended Citation:
Mouatadid S.,Raj N.,Deo R.C.,et al. Input selection and data-driven model performance optimization to predict the Standardized Precipitation and Evaporation Index in a drought-prone region[J]. Atmospheric Research,2018-01-01,212
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